freud.locality.Voronoi¶

The freud.locality.Voronoi class uses voro++ to compute the Voronoi diagram of a set of points, while respecting periodic boundary conditions (which are not handled by scipy.spatial.Voronoi, documentation).

These examples are two-dimensional (with $$z=0$$ for all particles) for simplicity, but the Voronoi class works for both 2D and 3D data.

[1]:

import numpy as np
import freud
import matplotlib
import matplotlib.pyplot as plt


First, we generate some sample points.

[2]:

points = np.array([
[-0.5, -0.5, 0],
[0.5, -0.5, 0],
[-0.5, 0.5, 0],
[0.5, 0.5, 0]])
plt.scatter(points[:, 0], points[:, 1])
plt.title('Points')
plt.xlim((-1, 1))
plt.ylim((-1, 1))
plt.gca().set_aspect('equal')
plt.show()


Now we create a box and a Voronoi compute object.

[3]:

L = 2
box = freud.box.Box.square(L)
voro = freud.locality.Voronoi()


Next, we use the compute method to determine the Voronoi polytopes (cells) and the polytopes property to return their coordinates. Note that we use freud’s method chaining here, where a compute method returns the compute object.

[4]:

cells = voro.compute((box, points)).polytopes
print(cells)

[array([[-1., -1.,  0.],
[ 0., -1.,  0.],
[ 0.,  0.,  0.],
[-1.,  0.,  0.]]), array([[ 0., -1.,  0.],
[ 1., -1.,  0.],
[ 1.,  0.,  0.],
[ 0.,  0.,  0.]]), array([[-1.,  0.,  0.],
[ 0.,  0.,  0.],
[ 0.,  1.,  0.],
[-1.,  1.,  0.]]), array([[0., 0., 0.],
[1., 0., 0.],
[1., 1., 0.],
[0., 1., 0.]])]


The Voronoi class has built-in plotting methods for 2D systems.

[5]:

plt.figure()
ax = plt.gca()
voro.plot(ax=ax)
ax.scatter(points[:, 0], points[:, 1], s=10, c='k')
plt.show()


This also works for more complex cases, such as this hexagonal lattice.

[6]:

def hexagonal_lattice(rows=3, cols=3, noise=0, seed=None):
if seed is not None:
np.random.seed(seed)
# Assemble a hexagonal lattice
points = []
for row in range(rows*2):
for col in range(cols):
x = (col + (0.5 * (row % 2)))*np.sqrt(3)
y = row*0.5
points.append((x, y, 0))
points = np.asarray(points)
points += np.random.multivariate_normal(mean=np.zeros(3), cov=np.eye(3)*noise, size=points.shape[0])
# Set z=0 again for all points after adding Gaussian noise
points[:, 2] = 0

# Wrap the points into the box
box = freud.box.Box(Lx=cols*np.sqrt(3), Ly=rows, is2D=True)
points = box.wrap(points)
return box, points

[7]:

# Compute the Voronoi diagram and plot
box, points = hexagonal_lattice()
voro = freud.locality.Voronoi()
voro.compute((box, points))
voro

[7]:


For noisy data, we see that the Voronoi diagram can change substantially. We perturb the positions with 2D Gaussian noise. Coloring by the number of sides of each Voronoi cell, we can see patterns in the defects: 5-gons and 7-gons tend to pair up.

[8]:

# Compute the Voronoi diagram
box, points = hexagonal_lattice(rows=12, cols=8, noise=0.03, seed=2)
voro = freud.locality.Voronoi()
voro.compute((box, points))

# Plot Voronoi with points and a custom cmap
plt.figure()
ax = plt.gca()
voro.plot(ax=ax, cmap='RdBu')
ax.scatter(points[:, 0], points[:, 1], s=2, c='k')
plt.show()


We can also compute the volumes of the Voronoi cells. Here, we plot them as a histogram:

[9]:

plt.hist(voro.volumes)
plt.title('Voronoi cell volumes')
plt.show()


The Voronoi class also computes a freud.locality.NeighborList, where particles are neighbors if they share an edge in the Voronoi diagram. The NeighborList effectively represents the bonds in the Delaunay triangulation. The neighbors are weighted by the length (in 2D) or area (in 3D) between them. The neighbor weights are stored in voro.nlist.weights.

[10]:

nlist = voro.nlist
line_data = np.asarray([[points[i],
points[i] + box.wrap(points[j] - points[i])]
for i, j in nlist])[:, :, :2]
line_collection = matplotlib.collections.LineCollection(line_data, alpha=0.2)
plt.figure()
ax = plt.gca()
voro.plot(ax=ax, cmap='RdBu')